U.S. patent number 11,443,639 [Application Number 16/477,144] was granted by the patent office on 2022-09-13 for methods of generating a unmanned aerial vehicle migration trajectory, electronic devices and storage mediums.
This patent grant is currently assigned to MOUTONG SCIENCE AND TECHNOLOGY CO., LTD. The grantee listed for this patent is MOUTONG SCIENCE AND TECHNOLOGY CO., LTD. Invention is credited to Hui Huang.
United States Patent |
11,443,639 |
Huang |
September 13, 2022 |
Methods of generating a unmanned aerial vehicle migration
trajectory, electronic devices and storage mediums
Abstract
A method of generating a UAV migration trajectory includes:
obtaining a drawn path on a map and preprocessing the drawn path to
generate a first path; determining a candidate region of interest
and sample viewpoints in a three-dimensional space according to
sample points of the first path; determining a local candidate
according to the candidate region of interest and the sample
viewpoints, and obtaining a local candidate cost function;
generating a local migration trajectory according to a path between
different local candidates, and obtaining a local migration
trajectory cost function of the local migration trajectory; and
constructing a set travelling salesman problem according to the
local candidate cost function and the local migration trajectory
cost function, and solving the set travelling salesman problem to
obtain a global migration trajectory.
Inventors: |
Huang; Hui (Guangdong,
CN) |
Applicant: |
Name |
City |
State |
Country |
Type |
MOUTONG SCIENCE AND TECHNOLOGY CO., LTD |
Guangdong |
N/A |
CN |
|
|
Assignee: |
MOUTONG SCIENCE AND TECHNOLOGY CO.,
LTD (Guangdong, CN)
|
Family
ID: |
1000006555057 |
Appl.
No.: |
16/477,144 |
Filed: |
October 12, 2018 |
PCT
Filed: |
October 12, 2018 |
PCT No.: |
PCT/CN2018/109972 |
371(c)(1),(2),(4) Date: |
July 10, 2019 |
PCT
Pub. No.: |
WO2020/062338 |
PCT
Pub. Date: |
April 02, 2020 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20210295714 A1 |
Sep 23, 2021 |
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Foreign Application Priority Data
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|
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Sep 30, 2018 [CN] |
|
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201811163146.2 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G08G
5/0013 (20130101); G08G 5/0026 (20130101); G06F
17/18 (20130101); G08G 5/0043 (20130101); G08G
5/0039 (20130101) |
Current International
Class: |
G08G
5/00 (20060101); G06F 17/18 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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103901892 |
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104807457 |
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Jul 2015 |
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CN |
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104867142 |
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Aug 2015 |
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CN |
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105589471 |
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May 2016 |
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CN |
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106043694 |
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Oct 2016 |
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CN |
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107278262 |
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Oct 2017 |
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CN |
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107943072 |
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Apr 2018 |
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108109437 |
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Jun 2018 |
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CN |
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108496134 |
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Sep 2018 |
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CN |
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101894409 |
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Sep 2018 |
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KR |
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2017/150433 |
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Sep 2017 |
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WO |
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Other References
International Search Report, PCT/CN2018/109972, dated Jul. 2, 2019
(9 pages). cited by applicant.
|
Primary Examiner: Brushaber; Frederick M
Assistant Examiner: Yang; Elizabeth
Attorney, Agent or Firm: Hamre, Schumann, Mueller &
Larson, P.C.
Claims
What is claimed is:
1. A method of controlling an unmanned aerial vehicle (UAV),
comprising: drawing a drawn path on a map; obtaining, by an
electronic device, the drawn path on the map; sampling and
smoothing the drawn path to generate a first path; determining a
candidate region of interest and sample viewpoints in a
three-dimensional (3D) space according to sample points of the
first path; determining a local candidate according to the
candidate region of interest and the sample viewpoints, and
obtaining a local candidate cost function; generating a local
migration trajectory according to a path between different local
candidates, and obtaining a local migration trajectory cost
function of the local migration trajectory; constructing a set
travelling salesman problem according to the local candidate cost
function and the local migration trajectory cost function, and
solving the set travelling salesman problem to obtain a global
migration trajectory; controlling flying of the UAV based on the
global migration trajectory; and controlling a camera of the UAV to
capture images or videos along the global migration trajectory,
wherein the sample points of the first path refer to feature points
constituting the first path; and the sample viewpoints refer to
points selected in a vertical line perpendicular to a surface where
the sample points of the first path are located, wherein the
obtaining the drawn path on the map and sampling and smoothing the
drawn path to generate the first path comprises: calculating a
curvature of each point on the drawn path, and extracting a point
whose curvature is greater than a threshold as a feature point;
performing sampling processing on the feature points; and
generating the first path according to the sampled feature
points.
2. The method according to claim 1, wherein the determining the
candidate region of interest and the sample viewpoints in the
three-dimensional space according to the sample points of the first
path comprises: determining the candidate region of interest
according to visual interest values of a region of interest within
a preset range of the sample points of the first path; determining
a safe flight space according to the sample points of the first
path; and determining sample viewpoints in the three-dimensional
space according to the sample points of the first path and the safe
flight space.
3. The method according to claim 2, wherein the determining the
candidate region of interest according to the visual interest
values of the region of interest within the preset range of the
sample points of the first path comprises: determining the visual
interest values of the region of interest within the preset range
of the sample points of the first path; and determining the
candidate region of interest from a maximum value of the visual
interest values.
4. The method according to claim 2, wherein the determining the
safe flight space according to the sample points of the first path
comprises: obtaining a first altitude and a second altitude
configured relative to a ground where sample points of the first
path are located, an area between the first altitude and the second
altitude is a safe flight space; and adding a preset height to a
height on the candidate region of interest to update the first
height when the first height overlaps the candidate region of
interest.
5. The method according to claim 1, wherein the obtaining the local
candidate cost function comprises: calculating a view quality of
the local candidate from the candidate region of interest and the
sample viewpoints; and determining a local candidate cost function
based on the view quality of the local candidate.
6. The method according to claim 1, wherein the local migration
trajectory cost function is related to an orientation of the UAV, a
pitch angle of the camera, and a distance between the local
candidates.
7. The method according to claim 1, wherein the method further
comprises: determining a flight speed of the UAV at each local
candidate based on at least one of an orientation change rate and a
pitch change rate of the UAV in the global migration
trajectory.
8. The method according to claim 1, wherein the method further
comprises: determining a speed variation of the UAV at each local
candidate according to at least one of the UAV orientation change
rate and the pitch angle change rate in the global migration
trajectory; determining a speed maximum value of the migration of
the UAV between the local candidates according to the speed
variation of the UAV in each local candidate; and controlling the
flight speed of the UAV at each local candidate to be less than a
maximum speed.
9. An electronic device, comprising a memory and a processor, the
memory having computer program stored therein for controlling an
unmanned aerial vehicle (UAV) which, when executed by the
processor, causing the processor to provide the steps of: drawing a
drawn path on a map; obtaining, by an electronic device, the drawn
path on the map; sampling and smoothing the drawn path to generate
a first path; determining a candidate region of interest and sample
viewpoints in a three-dimensional space according to sample points
of the first path; determining a local candidate according to the
candidate region of interest and the sample viewpoints, and
obtaining a local candidate cost function; generating a local
migration trajectory according to a path between different local
candidates, and obtaining a local migration trajectory cost
function of the local migration trajectory; constructing a set
travelling salesman problem according to the local candidate cost
function and the local migration trajectory cost function, and
solving the set travelling salesman problem to obtain a global
migration trajectory; controlling flying of the UAV based on the
global migration trajectory; and controlling a camera of the UAV to
capture images or videos along the global migration trajectory,
wherein the sample points of the first path refer to feature points
constituting the first path; and the sample viewpoints refer to
points selected in a vertical line perpendicular to a surface where
the sample points of the first path are located, wherein the
obtaining the drawn path on the map and sampling and smoothing the
drawn path to generate the first path comprises: calculating a
curvature of each point on the drawn path, and extracting a point
whose curvature is greater than a threshold as a feature point;
performing sampling processing on the feature points; and
generating the first path according to the sampled feature
points.
10. The electronic device according to claim 9, wherein the
processor is further caused to perform the steps of: determining
the candidate region of interest according to visual interest
values of a region of interest within a preset range of the sample
points of the first path; determining a safe flight space according
to the sample points of the first path; and determining sample
viewpoints in the three-dimensional space according to the sample
points of the first path and the safe flight space.
11. The electronic device according to claim 10, wherein the
processor is further caused to perform the steps of: determining
the visual interest values of the region of interest within the
preset range of the sample points of the first path; and
determining the candidate region of interest from a maximum value
of the visual interest values.
12. The electronic device according to claim 10, wherein the
processor is further caused to perform the steps of: obtaining a
first altitude and a second altitude configured relative to a
ground where sample points of the first path are located, an area
between the first altitude and the second altitude is a safe flight
space; and adding a preset height to a height on the candidate
region of interest to update the first height when the first height
overlaps the candidate region of interest.
13. The electronic device according to claim 9, wherein the
processor is further caused to perform the steps of: calculating a
view quality of the local candidate from the candidate region of
interest and the sample viewpoints; and determining a local
candidate cost function based on the view quality of the local
candidate.
14. The electronic device according to claim 9, wherein the local
migration trajectory cost function is related to an orientation of
the UAV, a pitch angle of the camera, and a distance between the
local candidates.
15. The electronic device according to claim 9, the processor is
further caused to perform the steps of: determining a flight speed
of the UAV at each local candidate based on at least one of an
orientation change rate and a pitch change rate of the UAV in the
global migration trajectory.
16. The electronic device according to claim 9, the processor is
further caused to perform the steps of: determining a speed
variation of the UAV at each local candidate according to at least
one of the UAV orientation change rate and the pitch angle change
rate in the global migration trajectory; determining a speed
maximum value of the migration of the UAV between the local
candidates according to the speed variation of the UAV in each
local candidate; and controlling the flight speed of the UAV at
each local candidate to be less than the maximum speed.
17. One or more non-transitory computer-readable storage medium
comprising computer-executable instructions for controlling an
unmanned aerial vehicle (UAV) which, when executed by one or more
processors, causing the one or more processors to provide the steps
of: drawing a drawn path on a map; obtaining, by an electronic
device, the drawn path on the map; sampling and smoothing the drawn
path to generate a first path; determining a candidate region of
interest and sample viewpoints in a three-dimensional space
according to sample points of the first path; determining a local
candidate according to the candidate region of interest and the
sample viewpoints, and obtaining a local candidate cost function;
generating a local migration trajectory according to a path between
different local candidates, and obtaining a local migration
trajectory cost function of the local migration trajectory;
constructing a set travelling salesman problem according to the
local candidate cost function and the local migration trajectory
cost function, and solving the set travelling salesman problem to
obtain a global migration trajectory; controlling flying of the UAV
based on the global migration trajectory; and controlling a camera
of the UAV to capture images or videos along the global migration
trajectory, wherein the sample points of the first path refer to
feature points constituting the first path; and the sample
viewpoints refer to points selected in a vertical line
perpendicular to a surface where the sample points of the first
path are located, wherein the obtaining the drawn path on the map
and sampling and smoothing the drawn path to generate the first
path comprises: calculating a curvature of each point on the drawn
path, and extracting a point whose curvature is greater than a
threshold as a feature point; performing sampling processing on the
feature points; and generating the first path according to the
sampled feature points.
18. The computer-readable storage medium according to claim 17,
wherein the processor is further caused to perform the steps of:
determining the candidate region of interest according to visual
interest values of a region of interest within a preset range of
the sample points of the first path; determining a safe flight
space according to the sample points of the first path; and
determining sample viewpoints in the three-dimensional space
according to the sample points of the first path and the safe
flight space.
19. The computer-readable storage medium according to claim 18,
wherein the processor is further caused to perform the steps of:
determining the visual interest values of the region of interest
within the preset range of the sample points of the first path; and
determining the candidate region of interest from a maximum value
of the visual interest values.
20. The computer-readable storage medium according to claim 18,
wherein the processor is further caused to perform the steps of:
obtaining a first altitude and a second altitude configured
relative to a ground where sample points of the first path are
located, an area between the first altitude and the second altitude
is a safe flight space; and adding a preset height to a height on
the candidate region of interest to update the first height when
the first height overlaps the candidate region of interest.
Description
TECHNICAL FIELD
The present application relates to the field of UAV technology, and
in particular to methods of generating a UAV migration trajectory,
electronic devices and storage mediums.
BACKGROUND
The unmanned aerial vehicle (UAV) is a kind of intelligent flight
device which does not need the pilot to drive directly and is
controlled only by radio remote control or on-board program and has
the advantages of small volume, simple structure, low cost, and can
work in complex high altitude environment. With the development of
UAV technology, more and more people use UAV to take images and
aerial video.
However, most of the current aerial video is composed of a lot of
short and simple aerial camera clips, and these aerial video clips
are not continuous, because it is difficult to manipulate UAVs. At
the same time, the artificial trajectory of the UAV is rough, it is
difficult to use the UAV in a large, complex environment to make a
continuous aerial video.
SUMMARY
According to various embodiments of the present disclosure, methods
and apparatuses of generating a UAV migration trajectory,
electronic devices and storage mediums are provided.
A method of generating a UAV migration trajectory includes:
obtaining a drawn path on a map and preprocessing the drawn path to
generate a first path;
determining a candidate region of interest and sample viewpoints in
a three-dimensional space according to sample points of the first
path;
determining a local candidate according to the candidate region of
interest and the sample viewpoints, and obtaining a local candidate
cost function;
generating a local migration trajectory according to a path between
different local candidates, and obtaining a local migration
trajectory cost function of the local migration trajectory; and
constructing a set travelling salesman problem according to the
local candidate cost function and the local migration trajectory
cost function, and solving the set travelling salesman problem to
obtain a global migration trajectory.
An apparatus of generating a UAV migration trajectory includes:
a processing module configured to obtain a drawn path on a map and
preprocess the drawn path to generate a first path;
a determining module configured to determine a candidate region of
interest and sample viewpoints in a three-dimensional space
according to sample points of the first path;
a local candidate determining module configured to determine a
local candidate according to the candidate region of interest and
the sample viewpoints, and obtain a local candidate cost
function;
a local migration trajectory generating module configured to
generate a local migration trajectory according to a path between
different local candidates, and obtain a local migration trajectory
cost function of the local migration trajectory; and
a global migration trajectory generating module configured to
construct a set travelling salesman problem according to the local
candidate cost function and the local migration trajectory cost
function, and solve the set travelling salesman problem to obtain a
global migration trajectory.
An electronic device includes a memory and a processor, the memory
has computer program stored therein which, when executed by the
processor, cause the processor to provide the steps of: obtaining a
drawn path on a map and preprocessing the drawn path to generate a
first path; determining a candidate region of interest and sample
viewpoints in a three-dimensional space according to sample points
of the first path; determining a local candidate according to the
candidate region of interest and the sample viewpoints, and
obtaining a local candidate cost function; generating a local
migration trajectory according to a path between different local
candidates, and obtaining a local migration trajectory cost
function of the local migration trajectory; and constructing a set
travelling salesman problem according to the local candidate cost
function and the local migration trajectory cost function, and
solving the set travelling salesman problem to obtain a global
migration trajectory.
One or more non-transitory computer-readable storage medium
includes computer-executable instructions which, when executed by
one or more processors, cause the one or more processors to provide
the steps of: obtaining a drawn path on a map and preprocessing the
drawn path to generate a first path; determining a candidate region
of interest and sample viewpoints in a three-dimensional space
according to sample points of the first path; determining a local
candidate according to the candidate region of interest and the
sample viewpoints, and obtaining a local candidate cost function;
generating a local migration trajectory according to a path.
between different local candidates, and obtaining a local migration
trajectory cost function of the local migration trajectory; and
constructing a set travelling salesman problem according to the
local candidate cost function and the local migration trajectory
cost function, and solving the set travelling salesman problem to
obtain a global migration trajectory.
The details of one or more embodiments of the application are set
forth in the accompanying drawings and the description below. Other
features and advantages of the application will be apparent from
the description, drawings, and claims
BRIEF DESCRIPTION OF THE DRAWINGS
To illustrate the technical solutions according to the embodiments
of the present disclosure more clearly, the accompanying drawings
for describing the embodiments are introduced briefly in the
following. Apparently, the accompanying drawings in the following
description are only some embodiments of the present disclosure,
and persons of ordinary skill in the art can derive other drawings
from the accompanying drawings without creative efforts.
FIG. 1 is an application environment diagram of a method of
generating a UAV migration trajectory according to an
embodiment.
FIG. 2 is a flowchart of a method of generating a UAV migration
trajectory according to an embodiment.
FIG. 3 is a flowchart of a preprocessing step according to an
embodiment.
FIG. 4 is a flowchart of determining a candidate region of interest
and sample viewpoints according to another embodiment.
FIG. 5 is a schematic flowchart of a step of determining a safe
flight space according to an embodiment.
FIG. 6 is a flowchart of a method of generating a UAV migration
trajectory according to an embodiment.
FIG. 7 is a line chart of an evaluation result of an aerial video
according to an embodiment.
FIG. 8 is a line chart of an evaluation result of a preview scene
according to an embodiment.
FIG. 9 is a line chart of an evaluation result of a demand
expectation according to an embodiment.
FIG. 10 is a block diagram of an apparatus of generating a UAV
migration trajectory according to an embodiment.
FIG. 11 is a schematic diagram of an electronic device according to
an embodiment.
DETAILED DESCRIPTION OF THE EMBODIMENTS
The present disclosure will be described in details in combination
with the accompanying drawings and embodiments such that the
technical solution and advantages of the present disclosure will be
more apparent. It should be understood that the particular
embodiments are described for the purpose of illustrating as
opposed to restricting the present disclosure.
The method of generating a UAV migration trajectory provided in the
embodiment of the disclosure may be applied to the application
environment as shown in FIG. 1. The electronic device 102 may be,
but is not limited to, a variety of personal computers, notebook
computers, smartphones, tablet computers, and portable wearable
devices, or may be a server. The electronic device 102 may
individually implement the generation of the UAV migration
trajectory; or may be implemented by communication with other
electronic devices.
In an embodiment, as shown in FIG. 2, a method of generating a UAV
migration trajectory is provided, with the method applied to the
electronic device in FIG. 1 as an example, the method includes the
steps of:
In step 202: obtain a drawn path on the map, and preprocess the
drawn path to generate a first path.
The drawn path refers to a user's hand-drawn path on a map, which
may be, but is not limited to, a variety of paper maps, electronic
maps, and two-dimensional maps. The preprocessing refers to
smoothing and sampling processing on the hand-drawn path, and the
first path refers to the path after the smoothing and sampling
processing.
Specifically, by shooting by itself, the electronic device may
obtain a continuous hand-drawn path drawn on the map by the user, a
hand-drawn path received from other electronic devices, or may be a
hand-drawn path directly recorded on an electronic map. The
hand-drawn path is generally rough and not smooth, and the
electronic device needs to smooth the hand-drawn path and sample
points on the smoothed path so that the simplified path is close to
the original hand-drawn path. After preprocessing, the rough and
dense hand-drawn path is simplified into a sparse and smooth new
path. The new path is the first path, and the points on the first
path are called sample points.
In step 204: determine a candidate region of interest and sample
viewpoints in the three-dimensional space according to the sample
points of the first path.
The region of interest refers to the region that the UAV may shoot.
The candidate region of interest refers to a region of interest
having the highest visual interest value within a preset range of
each of the sample points of the first path. The sample viewpoints
refer to points selected in a vertical line perpendicular to the
surface where the sample points of the first path are located.
These sampling points represent possible positions of the UAV.
Specifically, the electronic device presets each of the sample
points on the first path with a range in which the region of
interest having the highest visual interest value is found as the
candidate region of interest. And the electronic device also
determines a safe flight space of the UAV based on each of the
sample points on the first path, the safe flight space is a region
between the minimum and maximum flight altitudes of the UAV. After
determining the safe flight space, the electronic device determines
sample viewpoints in the three-dimensional space according to the
sample points of the first path and the safe flight space.
In step 206: determine a local candidate according to the candidate
region of interest and the sample viewpoints, and obtain a local
candidate cost function.
The local candidate refers to the case where the UAV is shooting a
candidate region of interest at one sample viewpoint, and the local
candidate is determined according to the position and orientation
of the UAV, the position refers to the sample viewpoint the UAV is
located, and the orientation refers to the candidate region of
interest the UAV is shooting.
Specifically, one sample viewpoint has a plurality of candidate
regions of interest, and the UAV shooting different candidate
regions of interest at the sample viewpoints has different local
candidates. The electronic device determines a local candidate from
the candidate region of interest and the sample viewpoints, and
obtains a local candidate cost function. In the sample viewpoint A,
for example, there are five candidate regions of interest a, b, c,
d, e, the UAV may be shooting one of the candidate regions of
interest when sampling the viewpoint a, then, the case where the
UAV is shooting the candidate region of interest a at the sample
viewpoint A is a local candidate, and the case where the UAV is
shooting the candidate region of interest b at the sample viewpoint
A is another local candidate. In both cases, the position of the
UAV is the same, but the orientation is different, and it is
considered to be two different local candidates. Similarly, where
the UAV has the same orientation and different positions are
considered to be two different local candidates.
In step 208: generate a local migration trajectory according to a
path between different local candidates, and obtain a local
migration trajectory cost function of the local migration
trajectory.
The local migration trajectory refers to the migration path between
two different local candidates. The local migration trajectory cost
function is the function of finding the shortest migration
trajectory between local candidates.
Specifically, the electronic device generates the local migration
trajectory according to the path between two different local
candidates, and obtains a local migration trajectory cost function
of the local migration trajectory, the local migration trajectory
cost function is related to the position and orientation of the
UAV, the pitch angle of the camera, and the distance between the
local candidates.
In step 210: construct a set travelling salesman problem according
to the local candidate cost function and the local migration
trajectory cost function, and solve the set travelling salesman
problem to obtain a global migration trajectory.
The Set Traveling Salesman Problem (STSP) is a combination
optimization problem in which the migration path of UAV is the
shortest.
Specifically, the electronic device obtains the local candidate
cost function and the local migration trajectory cost function, and
converts the combined optimization model of the two functions into
a generalized version of the travelling salesman problem, which is
called a STSP. The optimal local candidates are obtained by solving
the set travelling salesman problem, and the global migration
trajectory is the sum of the migration trajectories of these
optimal local candidates.
In the method of generating the migration trajectory of the UAV,
the drawn path on the map is obtained by the electronic device and
is preprocessed to obtain the first path; the candidate region of
interest and the sample viewpoints are determined according to the
sample points of the first path; then the local candidate cost
function is determined and the local candidate cost function is
obtained; the local migration trajectory is determined according to
the path between different local candidates, and the local
migration trajectory cost function is obtained; and the set
travelling salesman problem is constructed and solved to obtain the
global migration trajectory. The UAV flies and shoots according to
the generated migration trajectory, and can shoot a safe and
continuous video in a large and complex environment.
In an embodiment, as shown in FIG. 3, the step of obtaining the
drawn path on the map and preprocessing the drawn path to generate
the first path includes: smooth and sample the hand-drawn path and
generate the first path according to the points after sample
processing.
In step 302: calculate a curvature of each point on the drawn path,
and extract a point whose curvature is greater than a threshold as
a feature point.
The curvature is the rotation rate of the arc length with respect
to the tangent direction angle of a certain point on the drawn
path, indicating the degree of deviation of the drawn path from the
straight line, and the larger the curvature is, the greater the
bending degree of the drawn path.
Specifically, the step of smoothing the drawn path by the
electronic device includes obtaining a point on the map
constituting the drawn path, calculating a curvature of each point,
and setting the point as a feature point if the curvature of the
point is greater than a threshold value. For example, the
electronic device calculates the curvature of each point of the
drawn path using the finite difference method, assuming that the
maximum curvature value of all points is c, the default setting
value of 0.1c is a threshold value, and a point greater than the
threshold value is set as a feature point, points smaller than the
threshold value are removed from the drawn path, and the path
obtained from the retained feature points is the smoothed path.
In step 304: perform sampling processing on the feature points.
Specifically, the electronic device may sample the feature points
retained, in the drawn path in an equal-distance sampling manner,
and select the feature points at the same distance intervals; and a
feature-preserving morphological sampling method may also be
adopted, i.e., selecting feature points capable of drawing the
shape of the path, such as feature points where the path changes
from a straight line to a curve, are selected when the direction of
the path changes, the path formed by these selected feature points
is closest to the original drawn path.
In step 306: generate a first path according to the sampled feature
points.
Specifically, the new path generated according to the sampled
feature points is referred to as a first path, and the first path
after the sampling process is simplified from a rough dense path to
a sparse and smooth path.
In the method of generating a UAV migration trajectory, the feature
points are obtained by calculating the curvatures of the points on
the drawn path, and the feature points are sampled, so that the
rough and dense path is simplified to a sparse and smooth path, the
memory space of the electronic device is saved.
In an embodiment, as shown in FIG. 4, the step of determining the
candidate region of interest and the sample viewpoints in the
three-dimensional space according to the sample points of the first
path includes:
In step 402: determine the candidate region of interest according
to visual interest values of a region of interest within a preset
range of the sample points of the first path.
The sample points of the first path refers to feature points
constituting the first path. The preset range refers to an area
within a preset distance from the center with a sample point as the
center. A visual interest value refers to the number of regions of
interest.
Specifically, the electronic device selects a region of interest
within a preset distance from the center with each of the sample
points of the first path as the center, and the number of the
regions of interest is a visual interest value. Within the preset
range of the sample points, the region of interest having the
highest visual interest value serves as a candidate region of
interest for the sample point. For example, for each of the sample
points, the region of interest is selected within a range of 150
meters of the point, in the ranges of the sample points, the
maximum number of regions of interest is 5, the maximum value of
the visual interest value of the region of interest is equal to 5,
the 5 regions of interest are used as candidate regions of interest
for all sample points.
In step 404: determine a safe flight space according to the sample
points of the first path.
The safe flight space refers to the area between the minimum
altitude and the maximum altitude that the UAV can fly.
Specifically, the electronic device sets the safe flight space of
the UAV in the vertically upper region of the sample points of the
first path, with reference to the ground where the sample points of
the first path are located, sets an altitude vertically above the
ground at which the sample points is located as a minimum altitude
for the UAV, and sets another altitude as a maximum altitude for
the UAV. The area between the minimum altitude and the maximum
altitude set vertically above the ground where the sample points on
the first path are located is the safe flight space in which the
UAV can only fly.
In step 406: determine sample viewpoints in the three-dimensional
space according to the sample points of the first path and the safe
flight space.
Specifically, the electronic device selects sample viewpoints in
the safe flight space above the sample points of the first path,
the sampling strategy may, but is not limited to, sample every 10
meters between a minimum and maximum safety altitude, for example,
if the minimum height of the safe flight space is 30 meters and the
maximum height is 120 meters, the point at 40 meters and the point
at 50 meters from the vertically upper region of the sample points
of the first path are respectively the first sample viewpoint and
the second sample viewpoint. Each of the sample points of the first
path corresponds to a plurality of sample viewpoints in the safe
flight space, and the sample viewpoints are different from each
other by a certain distance, which ensures that the UAV has no
collision at each sample viewpoints.
In an embodiment, the step of determining the safe flight space
according to the sample points of the first path includes:
Obtain a first altitude and a second altitude configured relative
to the ground where sample points of the first path are located, an
area between the first altitude and the second altitude is a safe
flight space; and
Add a preset height to a height on the candidate region of interest
to update the first height when the first height overlaps the
candidate region of interest.
The first height is the height of the ground from where the sample
points on the first path are located, the first height is the
minimum height for the UAV flight. The second altitude is another
altitude from the ground at which the sample points on the first
path are located, the second altitude is the maximum altitude at
which the UAV is flying.
Specifically, the electronic device obtains a first altitude and a
second altitude of the UAV flight, the first altitude overlaps the
candidate region of interest when the first altitude is less than
the altitude of the candidate region of interest, then a preset
height is added to the height on the candidate region of interest
as a new first height. As shown in FIG. 5, the electronic device
sets the ground at the position of the sample points a, b, c, d, e
on the first path as a reference, and sets a height of 30 meters
vertically above the ground at which each of the sample points is
located as the first height, and the height of 120 meters from the
ground is the second height. However, the position of the sample
point a of the first path overlaps with the candidate region of
interest A, the height on the candidate region of interest A is 40
meters, then the height of 40 meters of the candidate region of
interest a is increased by 20 meters as the first height of 60
meters corresponding to the ground where the sample points a is
located, while to the other sample points b, c, d, the first height
corresponding to the ground where e is located is still 30 meters,
and the second height corresponding to the ground where all sample
points are located is constant. The safe flight space of the UAV is
the area between the first altitude and the second altitude
corresponding to the ground where each of the sample points is
located. The safe flight space is determined by determining the
minimum flight altitude and the maximum flight altitude of the UAV
with respect to the ground where the sample points of the first
path are located, so as to ensure the safety of the flight of the
UAV.
In an embodiment, the step of obtaining the local candidate cost
function includes:
Calculating a view quality of the local candidate from the
candidate region of interest and the sample viewpoints; and
Determining a local candidate cost function based on the view
quality of the local candidate.
The local candidate view quality refers to the image quality of the
UAV at one sampling point toward the center of each candidate
region of interest.
Specifically, the electronic device calibrates the camera on the
UAV to determine the values of the internal and external parameters
of the camera. The internal parameters of the camera may include
f.sub.x, f.sub.y, c.sub.x, c.sub.y, f.sub.x is the unit pixel size
of the focal length in the x-axis direction of the image coordinate
system; f.sub.y is the unit pixel size of the focal length in the
y-axis direction of the image coordinate system; and c.sub.x,
c.sub.y are the principal point coordinates of the image plane. The
principal point is the intersection of the camera optical axis and
the image plane, f.sub.x=f/d.sub.x, f.sub.y=f/d.sub.y, f is the
focal length of the single camera, d.sub.x is the width of one
pixel in the x-axis direction of the image coordinate system, and
d.sub.y is the width of one pixel in the y-axis direction of the
image coordinate system. The image coordinate system is a
coordinate system established with reference to a two-dimensional
image captured by a camera, and is used to specify a position of an
object in the captured image. The origin of the (x, y) coordinate
system in the image coordinate system is located at the focal point
(c.sub.x, c.sub.y) of the camera optical axis and the imaging plane
in units of length, that is, meters, the origin of the (u, v)
coordinate system in the pixel coordinate system is in the upper
left corner of the image and in units of quantities, i.e., numbers.
And (x, y) is for characterizing the perspective projection
relationship of the object from the camera coordinate system to the
image coordinate system and (u, v) is for characterizing the pixel
coordinates. The conversion relationship between (x, y) and (u, v)
is as follows:
.times..times..times..times. ##EQU00001##
The perspective projection is a kind of one-sided projection which
is close to the visual effect by projecting the shape onto the
projection surface by the central projection method.
The external parameters of the camera include a rotation matrix and
a translation matrix in which coordinates in the world coordinate
system are converted to coordinates in the camera coordinate
system. The world coordinate system reaches the camera coordinate
system through rigid body transformation, and the camera coordinate
system reaches the image coordinate system through perspective
projection transformation. Rigid body transformation refers to the
motions of rotation and translation on a geometric object when the
object is not subject to deformation in three-dimensional space,
i.e., rigid body transformation. Rigid body transformation is as
shown in Equation (2):
.function..times..times..times..times..times..times..times..times..times.
##EQU00002##
X.sub.c is a camera coordinate system, X is a world coordinate
system, R is a rotation matrix from the world coordinate system to
the camera coordinate system, and T is a translation matrix from
the world coordinate system to the camera coordinate system are
represented. The distance between the origin of the world
coordinate system and the origin of the camera coordinate system is
mutually controlled by the components in the directions of x, y,
and z axes, and has three degrees of freedom, R is the sum of the
effects of rotating around the X, Y, and Z axes, respectively,
t.sub.x is the amount of translation in the x axis direction,
t.sub.y is the amount of translation in the y axis direction, and
t.sub.z is the amount of translation in the z axis direction.
The world coordinate system is the absolute coordinate system of
the objective three-dimensional space and can be established at any
position. For example, for each calibration image, the world
coordinate system may be established with the upper left corner of
the calibration plate as the origin, the calibration plate plane as
the XY plane, and the Z axis perpendicular to the calibration plate
plane upward. The camera coordinate system takes the camera optical
center as the origin of the coordinate system, and the optical axis
of the camera as the Z-axis, and the X-axis and the Y-axis are
respectively parallel to the X-axis and Y-axis of the image
coordinate system. The principal point of the image coordinate
system is the intersection of the optical axis and the image plane.
The image coordinate system takes the main point as the origin. The
pixel coordinate system means that the origin is defined at the
upper left corner of the image plane. The electronic device uses
the virtual camera with the same internal and external parameters
of the camera on the UAV to render the current angle of view to
obtain a visual interest map I(x).
At the same time, a weight graph I.sup.w is calculated based on the
central method of aerial photography aesthetic rules and the
trichotomy method, in which the central method refers to the
composition method in which the subject is placed at the center of
the picture. The trichotomy, also known as the well-character
mapping method, is a composition method in which the scene is
divided into 9 squares by two horizontal lines and two vertical
lines to obtain 4 intersections, and then the emphasis to be
expressed is placed in any one of the intersections. The point
multiplication result Q(x)=I(x)I.sup.w of the visual interest map
I(x) and the weight map I.sup.w is the view quality of the UAV in
the local candidate. Then, a local candidate cost function is
obtained according to the view quality Q(x) of the local candidate,
and the local candidate cost function can be expressed as.
E.sub.local(x)=1-Q(x) Equation (3)
In an embodiment, the cost function of the local migration
trajectory is related to the orientation of the UAV, the pitch
angle of the camera, and the distance between the local
candidates.
Specifically, the cost function of the local migration trajectory
may be expressed as:
E.sub.trans(T.sub.(i,j),(i+1,k)=w.sub.h*E.sub.hH.sub.p+w.sub.p*E.sub.p
Equation (4)
T.sub.(i,j),(i+1,k) is the transition locus between the j-th
partial candidate of the i-th hand-drawn sample points to the k-th
partial candidate of the i+1-th hand-drawn sample points is shown.
E.sub.k represents an orientation change rate between the local
candidates x.sub.(i, j) and x.sub.(i+1, j), which may be expressed
as:
.function..function..function. ##EQU00003## h(x) is the orientation
of the local candidate x, d(x.sub.(i,j),x.sub.(i+1,k)) is the
distance between the two local candidates. H.sub.p is a penalty
term indicating that the orientation of the UAV changes too much
between the two local candidates, which can be expressed as
.function..function..function. ##EQU00004## E.sub.p is a change
rate in camera pitch angle between local candidates x.sub.(i, j)
and x.sub.(i+1, k), the change rate in camera pitch angle may be
expressed as:
.function..function..function. ##EQU00005## P (x) is the pitch
angle of the local candidate camera. w.sub.k is the weight of the
UAV's orientation, w.sub.p is the camera's pitch angle, w.sub.k and
w.sub.p are preset values, w.sub.k=0.8, w.sub.p=0.2. By determining
the local migration trajectory cost function through the
orientation of the UAV, the camera pitch angle and the distance
between the local candidates, the optimal local candidate can be
obtained by combining the local candidate cost function, thereby
obtaining the UAV migration trajectory that can shoot safe and
continuous videos.
In an embodiment, the method of generating a UAV migration
trajectory further includes:
Determining the flight speed of the UAV at each local candidate
according to at least one of the UAV orientation change rate and
the pitch angle change rate in the global migration trajectory.
Specifically, the electronic equipment obtains the global migration
trajectory of the UAV and calculates the flight speed of each local
candidate of the UAV, and the calculation method can be expressed
as follows:
.times..times..function..lamda..function..times..times..function..functio-
n..function..function..times..ltoreq..ltoreq..times..times.
##EQU00006##
i is the local candidate, w(i) is the speed variation amount of the
local candidate i, h.sub.i is the orientation of the UAV, and
p.sub.i is the pitch angle of the camera of the UAV. The weighting
function w(i) controls the increasing or decreasing of the speed so
that the UAV can fly at an appropriate speed in the event of a
change in orientation or a change in the pitch angle of the camera.
.lamda.(v.sub.i-v.sub.i-1).sup.2 is a smooth term, which makes the
speed variation be to smooth and reasonable when the UAV
transitions between different local candidate migration
trajectories. By calculating the speed of the UAV, the speed of the
UAV at each local candidate is determined, and the variation of the
UAV speed is smooth and reasonable, so that the visual stability of
the aerial video is not affected by the excessive speed variation
rate of the UAV in flight.
In an embodiment, as shown FIG. 6, a method of generating a UAV
migration trajectory includes:
In step 602, obtain a drawn path on a map, a curvature of each
point on the drawn path is calculated, and extract a point whose
curvature is greater than a threshold value as a feature point.
In step 604: perform sampling processing on the feature points, and
generate a first path according to the sampled feature points.
In step 606: determine a candidate region of interest according to
the visual interest value of the region of interest within a preset
range of the sample points of the first path.
In step 608: obtain a first altitude and a second altitude
configured relative to a ground where sample points of the first
path are located, an area between the first altitude and the second
altitude is a safe flight space.
In step 610: add a preset height to a height on the candidate
region of interest to update the first height when the first height
overlaps the candidate region of interest.
In step 612: determine sample viewpoints in the three-dimensional
space according to the sample points of the first path and the safe
flight space.
In step 614: determine a local candidate according to the candidate
region of interest and the sample viewpoints, and calculate a view
quality of the local candidate.
In step 616: determine a local candidate cost function according to
a view quality of the local candidate.
In step 618: generate a local migration trajectory according to a
path between different local candidates, and obtain a local
migration trajectory cost function of the local migration
trajectory.
In step 620: construct a set travelling salesman problem according
to the local candidate cost function and the local migration
trajectory cost function, and solve the set travelling salesman
problem to obtain a global migration trajectory.
In an embodiment, the solution is described according to an
application example of the method of generating a UAV migration
trajectory.
The UAV control system can use the software platform based on DJI
WayPointMission SDK (Software Development Kit) to control UAV
flight and shoot video. Using this SDK, you can specify the
waypoint sequence and accurately specify the UAV's physical
location, orientation, and camera pitch angle at each waypoint, as
well as the UAV's speed. There is a slight error between the actual
flight trajectory and the planned aerial migration trajectory,
which is mainly caused by the unstable GPS signal and the influence
of wind speed. The portable but powerful UAV's DJI Phantom 4 Pro
V2.0 can be used to shoot aerial video in the field. The flight
movements of the UAV includes moving forward, backward, left or
right along the horizontal axis, increasing and decreasing its
height, and changing its direction clockwise or counterclockwise.
The UAV is equipped with a 4K/30 fps 12 megapixel camera that can
tilt between 0 and 90 degrees. The UAV will fly between waypoints,
perform actions at waypoints, and adjust the orientation, altitude,
pitch and speed between waypoints. The actual migration trajectory
of the UAV is basically the same as the migration trajectory
generated by the system.
The electronic device obtains an aerial photographing result of a
path obtained by a user using two methods, one is a path generated
by the method of the present solution, and the other is a path
designed using a DJI GS PRO tool. According to the designed
questions and the user's answers, the results of aerial photographs
of the paths generated by the two methods are compared. In the
specific process, six users first use the method of the embodiment
of the application to obtain the global migration trajectory of the
UAV, and then use the DJI GS PRO tool to design a similar migration
trajectory, including specifying the waypoint position, set the
altitude, orientation and pitch angle of the UAV at each waypoint,
so that the UAV can take aerial video along the generated UAV
migration trajectory and the trajectory designed by DJI GS PRO.
The users' usage ratings are obtained. By providing some aerial
video to the users to judge, and collecting the users' answers to
evaluate and analyze aerial photography results of the paths
generated by the two methods. The questions offered to the user
are: 1) a more enjoyable aerial video, 2) a better preview of the
scene, 3) more desirable to the user. The options provided to the
user are: 1) the left video is more enjoyable, 2) the left video
provides a better preview of the entire scene, 3) the left video is
more desirable. The answers provided to the user are: 1) totally
agree, 2) basically agree, 3) neutral, 4) basically disagree, 5)
totally disagree. See Table 1 and FIG. 7, FIG. 8 and FIG. 9 for
evaluation results.
FIG. 7, FIG. 8, and FIG. 9 show the evaluation results of Problems
1) to 3). FIG. 7 shows the result of the selection of Problem 1)
that the video captured according to the UAV migration trajectory
generated according to the embodiment of the application is
superior to the DJI GS PRO version. According to the feedbacks of
the users, when using the DJI GS PRO, most users find it difficult
to estimate the height, orientation, and pitch angle that should be
set for photographing the candidate region of interest. According
to the video results, the UAV can only capture part of the
candidate region of interest when flying along some trajectory
designed by DJI GS PRO, and even difficult to capture the candidate
region of interest. For the same reason, for Problem 3), as shown
in FIG. 9, most users believe that the aerial shot video of the UAV
migration trajectory generated according to the embodiments of the
application better satisfies their needs. For problem 2), it can be
seen from FIG. 8 that the embodiments of the application provides a
preview scene better than the DJI GS PRO version, and when the
scene is more complex, DJI GS PRO may only consider shooting
specific regions of interest when planning the path. However, the
method of generating a UAV migration trajectory of the embodiments
of the application consider K regions of interest with the highest
visual interest value within 150 meters of the sample points, and
comprehensively consider the visual interest value, the direction
change and the pitch angle change, and then determines the optimum
altitude, orientation and pitch angle for each waypoint.
TABLE-US-00001 TABLE 1 Statistical Table of Time and Battery
Consumption of the Embodiments of the Application and DJI GS PRO;
Time consumption Battery consumption Embodiments Embodiments of the
of the Users Application DJI GS PRO Application DJI GS PRO 1 2 s +
4 s 20 m 32% (1) 120% (2) 2 3 s + 4 s 40 m 38% (1) 300% (5) 3 1 s +
2 s 15 m 18% (1) 144% (3) 4 1 s + 2 s 10 m 12% (1) 130% (3) 5 2 s +
4 s 30 m 40% (1) 160% (3) 6 2 s + 4 s 32 m 35% (1) 175% (4)
In the battery consumption; 100% means that one battery is
consumed. The number of times in parentheses indicates the user's
path modification and the number of reshoots. Some quantitative
statistics is carried out on the uses of the system constructed
according to the methods of the embodiments of the application and
of the DJI GS PRO to collect statistics of time consumption and
battery consumption. For embodiments of the application, the time
consumption includes a time for the user to include drawing a
sketch path and a calculation time required to generate a
trajectory. For the DJI GS PRO, the time consumption includes the
time that the user sets each waypoint and specifies the altitude,
orientation, and pitch angle for each waypoint.
From the statistical results in Table 1, it can be seen that the
time consumption of the embodiments of the application is short,
and the time consumed by using the DJI GS PRO tool is relatively
large, mainly because the user needs to repeatedly set the UAV
configuration information at each waypoint. From the comparison of
battery consumption, it can be seen that embodiments of the
application save a large amount of battery energy. Most users who
use the DJI GS PRO need to modify the path and re-shoot the video 2
to 5 times because of poor aerial photography.
In the above embodiment, by evaluating and analyzing the result of
aerial photography of the paths generated by the user use two
methods, the advantages of the application with respect to other
solutions can be clearly demonstrated, and the advantages of the
method of generating a UAV migration trajectory provided by the
application is further demonstrated.
It should be understood that although the steps in FIGS. 1-9 are
sequentially displayed as indicated by arrows, these steps are not
necessarily sequentially performed as indicated by arrows. Unless
explicitly stated herein, the execution of these steps is not
strictly sequential, and the steps may be performed in other
sequences. Moreover, at least a part of the steps in FIGS. 1-9 may
include a plurality of sub-steps or stages that are not necessarily
performed at the same time, but may be performed at different
times, and the order of execution of the sub-steps or stages is not
necessarily performed sequentially, but may be performed in turn or
alternately with at least a part of other steps or sub-steps or
stages of other steps.
In an embodiment, as shown in FIG. 10, an apparatus of generating a
UAV migration trajectory is provided, which includes a processing
module 1002, a determining module 1004, a local candidate
determining module 1006, a local migration trajectory generating
module 1008, and a global migration trajectory generating module
1010:
The processing module 1002 is configured to obtain a drawn path on
a map and preprocess the drawn path to generate a first path;
The determining module 1004 configured to determine a candidate
region of interest and sample viewpoints in a three-dimensional
space according to sample points of the first path;
The local candidate determining module 1006 is configured to
determine a local candidate according to the candidate region of
interest and the sample viewpoints, and obtain a local candidate
cost function;
The local migration trajectory generating module 1008 is configured
to generate a local migration trajectory according to a path
between different local candidates, and obtain a local migration
trajectory cost function of the local migration trajectory; and
The global migration trajectory generating module 1010 is
configured to construct a set travelling salesman problem according
to the local candidate cost function and the local migration
trajectory cost function, and solve the set travelling salesman
problem to obtain a global migration trajectory.
In the apparatus of generating the migration trajectory of the UAV,
the drawn path on the map is obtained by the electronic device and
is preprocessed to obtain the first path; the candidate region of
interest and the sample viewpoints are determined according to the
sample points of the first path; then the local candidate cost
function is determined and the local candidate cost function is
obtained; the local migration trajectory is determined according to
the path between different local candidates, and the local
migration trajectory cost function is obtained; and the set
travelling salesman problem is constructed and solved to obtain the
global migration trajectory. The UAV flies and shoots according to
the generated migration trajectory; and can shoot a safe and
continuous video with aerial esthetics.
In an embodiment, the processing module 1002 is further configured
to calculate a curvature of each point on the drawn path, and
extract a point whose curvature is greater than a threshold as a
feature point; perform sampling processing on the feature points;
and generate the first path according to the sampled feature
points. In the method of generating the UAV migration trajectory,
the feature points are obtained by calculating the curvatures of
the points on the drawn path, and the feature points are sampled,
so that the rough and dense path is simplified into a sparse and
smooth path, the calculation amount is reduced, and the memory
space of the electronic device is saved.
In an embodiment, the determining module 1004 is further configured
to determine the candidate region of interest according to visual
interest values of a region of interest within a preset range of
the sample points of the first path; determine a safe flight space
according to the sample points of the first path; and determine
sample viewpoints in the three-dimensional space according to the
sample points of the first path and the safe flight space. In the
apparatus, the safe flight space is determined by sample points of
the first path, and a plurality of sample viewpoint are determined
in the safe flight space, and the sample viewpoints are different
from each other by a certain distance, thereby ensuring that the
UAV has no collision on at each sample viewpoint.
In an embodiment, the determining module 1004 is further configured
to obtain a first altitude and a second altitude configured
relative to a ground where sample points of the first path are
located, an area between the first altitude and the second altitude
is a safe flight space; and add a preset height to a height on the
candidate region of interest to update the first height when the
first height overlaps the candidate region of interest. The safe
flight space is determined by determining the minimum flight
altitude and the maximum flight altitude relative to a ground where
sample points of the first path are located, so as to ensure the
safety of the flight of the UAV.
In an embodiment, the determining module 1006 is further configured
to calculate a view quality of the local candidate from the
candidate region of interest and the sample viewpoints; and
determine a local candidate cost function based on the view quality
of the local candidate. The local candidate cost function is
determined by determining the view quality of the local candidate,
which reduces the amount of computation and improves the efficiency
of the operation of the electronic device.
In an embodiment, the apparatus of generating a UAV migration
trajectory further includes a speed controlling module, configured
to determine a flight speed of the UAV at each local candidate
based on at least one of an orientation change rate and a pitch
change rate of the UAV in the global migration trajectory. The
speed controlling module of the apparatus of generating the UAV
migration trajectory determines the speed of the UAV at each local
candidate through the calculation of the UAV speed, and the change
of the UAV speed is smooth and reasonable, so as to avoid affecting
the visual stability of the aerial video shooting due to the
excessive change of the speed of flight of the UAV.
For the specific definition of the apparatus of generating a UAV
migration trajectory, reference can be made to the foregoing
definition of the method of generating a UAV migration trajectory
and is omitted for brevity. The various modules in the apparatus of
generating a UAV migration trajectory described above may be
implemented in whole or in part by software, hardware, and
combinations thereof. The modules may be embedded in or independent
from a processor in the computer device and in the form of
hardware, or may be stored in a memory in the computer device and
in the form of software to be called by the processor to perform
the operations corresponding to the modules.
In an embodiment, an electronic device is provided, the internal
structure diagram may be as shown in FIG. 11. The electronic device
includes a processor, a memory, a network interface, a display
screen, and an input device connected through a system bus. The
processor of the electronic device is configured to provide
computing and control capabilities. The memory of the electronic
device includes non-volatile storage medium and a Random Access
Memory (RAM). The non-transitory storage medium stores an operating
system and a computer program. The RAM provides an environment for
the operation of an operating system and the computer program in
the non-volatile storage medium. The network interface of the
electronic device is configured to communicate with external
terminals via a network connection. The computer program is
executed by the processor to implement a method of generating a UAV
migration trajectory. The display screen of the electronic device
may be a liquid crystal display screen or an electronic ink display
screen, and the input device of the electronic device may be a
touch layer covered on the display screen, or be a key, a trackball
or a touch pad set on the housing of the electronic device, or may
be an external keyboard, touch pad or mouse.
Those skilled in the art will appreciate that the structure shown
in FIG. 11 is merely a block diagram of a portion of the structure
associated with the solution of the application, and does not
constitute a limitation on the electronic device to which the
solution of the present disclosure is applied, a particular
electronic device may include more or fewer components, or combine
certain components, or with a different arrangement of
components.
In an embodiment, an electronic device is provided, which includes
a memory and a processor, the memory has computer program stored
therein which, when executed by the processor, causes the processor
to provide the steps of: obtaining a drawn path on a map and
preprocessing the drawn path to generate a first path; determining
a candidate region of interest and sample viewpoints in a
three-dimensional space according to sample points of the first
path; determining a local candidate according to the candidate
region of interest and the sample viewpoints, and obtaining a local
candidate cost function; generating a local migration trajectory
according to a path between different local candidates, and
obtaining a local migration trajectory cost function of the local
migration trajectory; and constructing a set travelling salesman
problem according to the local candidate cost function and the
local migration trajectory cost function, and solving the set
travelling salesman problem to obtain a global migration
trajectory.
In an embodiment, an computer-readable storage medium is provided,
which includes a computer program which, when executed by the
processor, causes the processor to provide the steps of: obtaining
a drawn path on a map and preprocessing the drawn path to generate
a first path; determining a candidate region of interest and sample
viewpoints in a three-dimensional space according to sample points
of the first path; determining a local candidate according to the
candidate region of interest and the sample viewpoints, and
obtaining a local candidate cost function; generating a local
migration trajectory according to a path between different local
candidates, and obtaining a local migration trajectory cost
function of the local migration trajectory; and constructing a set
travelling salesman problem according to the local candidate cost
function and the local migration trajectory cost function, and
solving the set travelling salesman problem to obtain a global
migration trajectory.
Persons of ordinary skill in the art understand that all or part of
the processes in the methods of the foregoing embodiments may be
implemented by a computer program instructing relevant hardware.
The computer program may be stored in a non-transitory
computer-readable storage medium. When the computer program is
executed, flows of embodiments of the methods as described above
may be included. Any references to memory, storage, databases, or
other media used in the various embodiments provided herein may
include non-transitory and/or transitory memory. The non-transitory
memory may include a read only memory (ROM), a programmable ROM
(PROM), an electrically programmable ROM (EPROM), an electrically
erasable programmable ROM (EEPROM), or a flash memory. The
transitory memory may include a random access memory (RAM) or an
external cache memory. By way of illustration and not limitation,
RAM is available In a variety of forms such as static RAM (SRAM),
dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate
SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),
Memory Bus (Rambus) Direct RAM (RDRAM), Direct Memory Bus Dynamic
RAM (DRDRAM), Memory Bus Dynamic RAM (RDRAM) etc.
The foregoing respective technical features involved in the
respective embodiments can be combined arbitrarily, for brevity,
not all possible combinations of the respective technical features
in the foregoing embodiments are described, however, to the extent
they have no collision with each other, the combination of the
respective technical features shall be considered to be within the
scope of the description.
The foregoing implementations are merely specific embodiments of
the present disclosure, and are not intended to limit the
protection scope of the present disclosure. It should be noted that
any variation or replacement readily figured out by persons skilled
in the art within the technical scope disclosed in the present
disclosure shall all fall into the protection scope of the present
disclosure. Therefore, the protection scope of the present
disclosure shall be subject to the protection scope of the
claims.
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